R Notebook

https://map-rfun.library.duke.edu/01_georeference.html

# install.packages("RPostgreSQL")
# install.packages("compare")
# install.packages("igraph")
# install.packages("rworldmap")
# install.packages("maps")
# install.packages("ggplot2")
# install.packages("tidyverse")
# install.packages("sf")
# install.packages("mapview")
# install.packages("dplyr")
library("RPostgreSQL")

Loading required package: DBI

library("compare")

Attaching package: ‘compare’

The following object is masked from ‘package:base’:

    isTRUE

library("igraph")

Attaching package: ‘igraph’

The following object is masked from ‘package:compare’:

    compare

The following objects are masked from ‘package:stats’:

    decompose, spectrum

The following object is masked from ‘package:base’:

    union

library("maps")
library("ggplot2")
library("rworldmap")

Loading required package: sp
### Welcome to rworldmap ###
For a short introduction type :      vignette('rworldmap')

library("tidyverse")

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
✓ tibble  3.1.6     ✓ dplyr   1.0.9
✓ tidyr   1.2.0     ✓ stringr 1.4.0
✓ readr   2.1.2     ✓ forcats 0.5.1
✓ purrr   0.3.4     
── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::as_data_frame() masks tibble::as_data_frame(), igraph::as_data_frame()
x purrr::compose()       masks igraph::compose()
x tidyr::crossing()      masks igraph::crossing()
x dplyr::filter()        masks stats::filter()
x dplyr::groups()        masks igraph::groups()
x dplyr::lag()           masks stats::lag()
x purrr::map()           masks maps::map()
x purrr::simplify()      masks igraph::simplify()

library("sf")

Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.0

library("mapview")

Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

library("dplyr")

# Load the PostgreSQL driver, create a connection to the postgres database, and employ it.
drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, dbname = "mycodb", host = "localhost", port = 5432, user = "ruser", password = "ruser")
sql_string <- paste("SELECT * FROM mycodb", sep="")
MycoDB <- data.frame(dbGetQuery(con, sql_string))

#Set the working directory for files.
FileDirectory <- paste("/home/daiten/Programming/R/Projects/MycoDB/Graphics/", sep="")

Let’s map it all.

# get map
worldmap <- getMap(resolution = "coarse")
# plot world map
plot(worldmap, col = "lightgrey", fill = T, border = "darkgray", xlim = c(-180, 180), ylim = c(-90, 90), bg = "aliceblue", asp = 1, wrap=c(-180,180))


map()

starbucks <- read.csv("https://raw.githubusercontent.com/libjohn/mapping-with-R/master/data/All_Starbucks_Locations_in_the_US_-_Map.csv")
starbucksNC <- starbucks
starbucksNC

mapview(starbucksNC, xcol = "Longitude", ycol = "Latitude", crs = 4269, grid = FALSE)

# Neat piping
# starbucksNC <- starbucks  %>% 
#   filter(starbucks$State == "AZ")
# 
# starbucksNC %>% glimpse()

Now let’s test it with our own data. Make sure mapview doesn’t get any NA or non numeric characters for xcol or ycol…haha….don’t do it.

suillus <-  subset(MycoDB, MycoDB$fungalgenus == "rhizoglomus")
suillus1 <- subset(suillus, is.na(suillus$fung_lat) == FALSE)
suillus2 <- subset(suillus1, is.na(suillus1$fung_long) == FALSE)

suilluslat <- as.numeric(suillus2$fung_lat) 
suilluslon <- as.numeric(suillus2$fung_long)

mapview(data.frame(lat = suilluslat, lon = suilluslon, mycorrhizae = suillus2$mycorrhizaetype, plant = suillus2$plantfamily), xcol="lon", ycol="lat", grid = FALSE, crs = 4269)

# mapview(suillus2, xcol=as.numeric(suillus2$fung_long), ycol=as.numeric(suillus2$fung_lat) , grid = FALSE, crs = 4269)

Scraps

# chords <- rbind(lat = as.numeric(suillus1$fung_lat), lon = as.numeric(suillus1$fung_lon))

# chords <- 

# typeof(chords[,1])
# chords[,2]

# mapview(suillus1, cbind(lat = as.numeric(suillus1$fung_lat), lon = as.numeric(suillus1$fung_lon)), xcol="lon", ycol="lat", grid = FALSE, crs = NA)
mapview(data.frame( lat = suillus1$fung_lat, lon = suillus1$fung_lon), xcol="lon", ycol="lat", grid = FALSE, crs = NA)
# mapview(chords, xcol="lon", ycol="lat", grid = FALSE, crs = NA)
# mapview(suillus1, xcol=chords[,2], ycol=chords[,1], grid = FALSE, crs = NA)


# Neat piping
# starbucksNC <- starbucks  %>% 
#   filter(starbucks$State == "AZ")
# 
# starbucksNC %>% glimpse()